Remote health care services, such as performing diagnostic imaging in remote locations that otherwise may not have adequate health care facilities, are increasing. The remote health care practice area is growing, due in part to cost reduction, faster diagnosis and the overall efficiency provided by a partial decentralization of health care dispensaries.
In remote health care, a patient may be examined by a remote health care practitioner (RHCP) in a medical dispensary remote from a major medical center such as a hospital. The RHCP may perform a protocol for a diagnostic test and possibly some treatment under the guidance and supervision of a specialist located at the major medical center. Communication between the RHCP and the specialist may be accomplished by a variety of communication modalities, which may exhibit communications having dynamic bandwidth, that is, a varying Quality of Service (QoS). If the effect of the varying QoS on the communications between the RHCP and the specialist is not recognized, characterized, and compensated for, in some instances the overall process is less efficient and may result in difficult or improper diagnosis.
However, in diagnostic ultrasound imaging, for example, diagnostic ultrasound image quality has no agreed upon metric. Typically, communication engineers use computational measures such as a mean squared error computed over a region of pixel values. This type of image quality assessment may be called objective. Objective quality assessment will have shortcomings for the particular computational measure selected. For example, if the mean squared error is selected as the computational error, and this error is on average very low, the assumption is that the displayed image is probably true to the original. However, a low mean squared error can arise from noise corruption that is significant in only a small portion of the transmission and this can lead to a significant image artifact. At the other end of the image quality definition are the subjective measures. These measures are typically formed by the opinion scores of expert image interpreters. Empaneling a set of experts to assess the image quality of a routine image transmission is impractical.
Thus, with conventional structures and methods, streaming data for a remote ultrasound examination does not provide a reasonable expectation of delivering a smooth presentation of adequate image quality. Moreover, even if image quality could be reliably automated so that the quality level satisfied expert image interpreters, the end goal of a transmitted image is not the quality per se, but instead the value in facilitating forming a correct diagnosis of the patient's condition. These same issues are present in conventional image compression, in particular when certifying the admissibility of lossy compression techniques and, if allowed, a determination and specification of acceptable limits on the type and degree of loss incurred by a particular compression technique. As a result, when working with transmitted medical imagery, it is typical to rely on the transmission channel's bandwidth as the main, if not sole, parameter for use in estimating image quality, which may not perform satisfactorily, particularly for a communication channel having varying bandwidth.